• Journal of Power Electronics
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• v.15 no.2
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• pp.419-430
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• 2015

Flux estimation is a significant foundation of high-performance control for DC-excited synchronous motor. For almost all flux estimators, such as the flux estimator based on phase locked loop (PLL), DC drift causes fluctuations in flux magnitude. Furthermore, significant dynamic error may be introduced at transient conditions. To overcome these problems, this paper proposes an improved flux estimator for the PLL-based algorithm. Filters based on the generalized integrator are used to avoid flux fluctuation problems caused by the DC drift at the back electromotive force. Programmable low-pass filters are employed to improve the dynamic performance of the flux estimator, and the cutoff frequency of the filter is determined by the dynamic factor. The algorithm is verified by a 960V/1.6MW industrial prototype. Simulation and experimental results show that the proposed estimator can estimate the flux more accurately than the PLL-based algorithm in a cycloconverter-fed DC-excited synchronous machine vector control system.

• The Transactions of the Korean Institute of Power Electronics
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• v.22 no.3
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• pp.249-255
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• 2017

This paper presents air-gap control according to Y and Delta connections of a double-sided air-gap permanent magnet synchronous motor (DA-PMSM) with independent three-phase structure. In particular, the DA-PMSM used in this study can be applied to low-speed and high-torque applications, such as wind turbines, tidal power generations, and electric propulsion ships, because of its modular stators and a rotor with numerous permanent magnets. Unlike conventional three-phase machines, the DA-PMSM has a symmetrical configuration with double-sided air-gap. Therefore, Y/Delta winding connections and serial/parallel configurations between stator modules are possible. To identify the DA-PMSM operating characteristics, mathematical modeling is analyzed according to the Y/Delta connections. Moreover, air-gap control performances by applying the winding connection methods are verified through experimental results.

• Journal of Magnetics
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• v.20 no.1
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• pp.79-85
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• 2015

This paper presents a comparative study of a Tubular Linear Machine (TLM) with an Axially Magnetized Single-sided Permanent Magnet (AMSPM) and an Axially Magnetized Double-sided Permanent Magnet (AMDPM) based on analytical field calculations. Using a two-dimensional (2-D) polar coordinate system and a magnetic vector potential, analytical solutions for the flux density produced by the stator windings are derived. This technique is significant for the design and control implementation of electromagnetic machines. The field solution is obtained by solving Maxwell's equations in the simplified boundary value problem consisting of the air gap and coil. These analytical solutions are then used to estimate the self and mutual inductances. Two different types of machine are used to verify the validity of these model simplifications, and the analytical results are compared to results obtained using the finite element method (FEM) and experimental measurement.

• Nuclear Engineering and Technology
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• v.53 no.11
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• pp.3563-3579
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• 2021

Measured decay heat data of light water reactor (LWR) spent nuclear fuel (SNF) assemblies are adopted to train machine learning (ML) models. The measured data is available for fuel assemblies irradiated in commercial reactors operated in the United States and Sweden. The data comes from calorimetric measurements of discharged pressurized water reactor (PWR) and boiling water reactor (BWR) fuel assemblies. 91 and 171 measurements of PWR and BWR assembly decay heat data are used, respectively. Due to the small size of the measurement dataset, we propose: (i) to use the method of multiple runs (ii) to generate and use synthetic data, as large dataset which has similar statistical characteristics as the original dataset. Three ML models are developed based on Gaussian process (GP), support vector machines (SVM) and neural networks (NN), with four inputs including the fuel assembly averaged enrichment, assembly averaged burnup, initial heavy metal mass, and cooling time after discharge. The outcomes of this work are (i) development of ML models which predict LWR fuel assembly decay heat from the four inputs (ii) generation and application of synthetic data which improves the performance of the ML models (iii) uncertainty analysis of the ML models and their predictions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.89-93
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• 2014

Reviews are very important for potential consumer' making choices. They are also used by manufacturers to find problems of their products and to collect competitors' business information. But someone write fake reviews to mislead readers to make wrong choices. Therefore detecting fake reviews is an important problem for the E-commerce sites. Support Vector Machines (SVMs) are very important text classification algorithms with excellent performance. In this paper, we propose a new incremental algorithm based on weight and the extension of Karush-Kuhn-Tucker(KKT) conditions and Convex Hull for online Review Spam Detection. Finally, we analyze its performance in theory.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.31-40
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• 2021

The increasing number of credit card fraud cases has become a considerable problem since the past decades. This phenomenon is due to the expansion of new technologies, including the increased popularity and volume of online banking transactions and e-commerce. In order to address the problem of credit card fraud detection, a rule-based approach has been widely utilized to detect and guard against fraudulent activities. However, it requires huge computational power and high complexity in defining and building the rule base for pattern matching, in order to precisely identifying the fraud patterns. In addition, it does not come with intelligence and ability in predicting or analysing transaction data in looking for new fraud patterns and strategies. As such, Data Mining and Machine Learning algorithms are proposed to overcome the shortcomings in this paper. The aim of this paper is to highlight the important techniques and methodologies that are employed in fraud detection, while at the same time focusing on the existing literature. Methods such as Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), naïve Bayesian, k-Nearest Neighbour (k-NN), Decision Tree and Frequent Pattern Mining algorithms are reviewed and evaluated for their performance in detecting fraudulent transaction.

• The Journal of Information Systems
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• v.30 no.4
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• pp.203-226
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• 2021

Purpose The purpose of this study is to enhance the accuracy score of personal credit scoring using the convolutional neural networks and secure the transparency of the deep learning model using eXplainalbe Artifical Inteligence(XAI) technique. Design/methodology/approach This study built a classification model by using the convolutional neural networks(CNN) and applied a methodology that is transformation of numerical data to imaged data to apply CNN on personal credit data. Then layer-wise relevance propagation(LRP) was applied to model we constructed to find what variables are more influenced to the output value. Findings According to the empirical analysis result, this study confirmed that accuracy score by model using CNN is highest among other models using logistic regression, neural networks, and support vector machines. In addition, With the LRP that is one of the technique of XAI, variables that have a great influence on calculating the output value for each observation could be found.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.203-208
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• 2022

The occurrence of Type 2 Diabetes Mellitus (T2DM) is hoarding globally. All kinds of Diabetes Mellitus is controlled to disrupt over 415 million grownups worldwide. It was the seventh prime cause of demise widespread with a measured 1.6 million deaths right prompted by diabetes during 2016. Over 90% of diabetes cases are T2DM, with the utmost persons having at smallest one other chronic condition in UK. In valuation of contemporary applications of Big Data (BD) to Diabetes Medicare by sighted its upcoming abilities, it is compulsory to transmit out a bottomless revision over foremost theoretical literatures. The long-term growth in medicine and, in explicit, in the field of "Diabetology", is powerfully encroached to a sequence of differences and inventions. The medical and healthcare data from varied bases like analysis and treatment tactics which assistances healthcare workers to guess the actual perceptions about the development of Diabetes Medicare measures accessible by them. Apache Spark extracts "Resilient Distributed Dataset (RDD)", a vital data structure distributed finished a cluster on machines. Machine Learning (ML) deals a note-worthy method for building elegant and automatic algorithms. ML library involving of communal ML algorithms like Support Vector Classification and Random Forest are investigated in this projected work by using Jupiter Notebook - Python code, where significant quantity of result (Accuracy) is carried out by the models.

• Journal of IKEEE
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• v.26 no.2
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• pp.280-286
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• 2022

This paper presents implementation of rapid control prototype (RCP) for permanent magnet synchronous machines (PMSMs) using a digital signal processor (DSP) and the PLECS software. By utilization of auto code generation function in the PLECS, a current vector control algorithm for a PMSM drive system using a DSP as a control processor can be developed more efficiently. In this paper, a background of a model based design (MBD) and real time control are reviewed. Also, commercial RCP products compatible with DSP boards are introduced. At the end of the paper, experimental implementation of RCP for a PMSM drive is presented.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.271-281
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• 2022

Manual seed classification challenges can be overcome using a reliable and autonomous seed purity identification and classification technique. It is a highly practical and commercially important requirement of the agricultural industry. Researchers can create a new data mining method with improved accuracy using current machine learning and artificial intelligence approaches. Seed classification can help with quality making, seed quality controller, and impurity identification. Seeds have traditionally been classified based on characteristics such as colour, shape, and texture. Generally, this is done by experts by visually examining each model, which is a very time-consuming and tedious task. This approach is simple to automate, making seed sorting far more efficient than manually inspecting them. Computer vision technologies based on machine learning (ML), symmetry, and, more specifically, convolutional neural networks (CNNs) have been widely used in related fields, resulting in greater labour efficiency in many cases. To sort a sample of 3000 seeds, KNN, SVM, CNN and CNN-SVM hybrid classification algorithms were used. A model that uses advanced deep learning techniques to categorise some well-known seeds is included in the proposed hybrid system. In most cases, the CNN-SVM model outperformed the comparable SVM and CNN models, demonstrating the effectiveness of utilising CNN-SVM to evaluate data. The findings of this research revealed that CNN-SVM could be used to analyse data with promising results. Future study should look into more seed kinds to expand the use of CNN-SVMs in data processing.